ILRST 4740 Cornell: Data Mining and Machine Learning | StudySoup

PreparED Study Materials

ILRST 4740: Data Mining and Machine Learning

School: Cornell University

Number of Notes and Study Guides Available: 0

Videos

Calculating Confidence Interval for Educational TV Viewership
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Learn how to determine a 90% confidence interval for the proportion of people who watch educational television based on a survey. Understand how to utilize the z-score and interpret results, guiding decisions for a television company's publicity efforts.

Computing Probabilities: College Students & Computer Ownership
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Understand the probabilities of computer ownership among university first-year students through various scenarios. This video breaks down calculations for chances of ownership and non-ownership among random groups.

Which Confidence Level Produces the Widest Interval? Explained!
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Uncover the relationship between confidence levels and interval widths. Grasp how the range of confidence intervals broadens with increasing confidence. Understand the trade-offs in statistical certainty and estimation.

Crunching Car Sales Data: Size, Color, and Probability Insights
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Uncover the likelihood of different car combinations by size and color from a dealer's sales data. Learn how to calculate probabilities, from the chances of a small car being black to the odds a non-small car isn't gray. An insightful look into understanding car preferences.

Traffic Light Wait Time: Using Probability to Decode Delays
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Explore the mathematical intricacies behind calculating the waiting time for a motorist at traffic lights. Learn about normal random variables, conditional probabilities, and the challenges in determining specific waiting durations.

Poisson Distribution in Event Count & Uranium Discovery
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Explore the dynamics of Poisson distribution using both theoretical understanding and a practical application related to discovering uranium deposits. With the help of Bayes' formula and parameter adjustments, interpret various probabilities and outcomes for event occurrences.

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